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December 2011

Air Quality Modeling Technical Support Document:

NJ 126 Petition of September 17, 2010

Final Rule

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Air Quality Assessment Division

Research Triangle Park, NC 27711

October 2011
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Table of Contents 

I. Introduction......................................................................................3

II. EPA Modeling of Portland Emissions for 1-Hour SO2 NAAQS for Determination of 

Remedy...........................................................................................5

A. Emissions and Source Characteristics

B. Meteorological Data

C. Receptor and Terrain Data

D. EPA Remedy Modeling Results

III. Portland Generating Station Emission

Limits..........................................................................................13

IV. Demonstration of Adequacy of Remedy at Full and Reduced Operating Load.....15

V. References.....................................................................................19

Appendix A. Analysis of CALPUFF Model Performance Based on Inclusion of PRIME 

Downwash Option for Martin’s Creek...............................................20

.

Appendix B. Analysis of Model-to-Monitor Comparison for the Columbia, New Jersey

Ambient SO2 Monitor.................................................................26

Appendix C. Analysis Meteorological Variability.................................................30
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I. Introduction

On September 17, 2010, the New Jersey Department of Environmental Protection (NJDEP) 

submitted to EPA a section 126 petition which asserts that Sulfur Dioxide (SO2) emissions from 

the Portland Generating Station (Portland Plant or PGS) in Upper Mount Bethel Township, 

Northampton County, Pennsylvania significantly contribute to nonattainment and/or interfere 

with maintenance of the 1-hour SO2 National Ambient Air Quality Standards (NAAQS)1in New 

Jersey (NJ). The petition included both CALPUFF2and AERMOD3dispersion modeling for the 

1-hour SO2 NAAQS which shows violations of the NAAQS in New Jersey based on impacts 

from the Portland Plant. NJDEP specifically petitioned the USEPA to “directly regulate the 

Portland Plant to abate the significant contribution to nonattainment and interference with New 

Jersey’s maintenance of, the more stringent 1-hour SO2 NAAQS.” See page 7, September 17, 

2010 petition.

Dispersion modeling results of ambient impacts from Portland submitted by NJDEP show 

modeled SO2 concentrations from ~7 times higher than the 1-hour SO2 NAAQS based on 

AERMOD modeling, to ~17 times higher based on CALPUFF modeling. As summarized in 

Section IV of the final rule preamble, and documented in detail in the air quality modeling TSD 

for the proposed rule4(proposed rule Modeling TSD), the EPA determined that NJDEP had not 

adequately demonstrated that CALPUFF performs better for this application than AERMOD, 

and therefore the EPA finding and final remedy for Portland to eliminate significant contribution 

to nonattainment and interference with maintenance of the 1-hour SO2 NAAQS in New Jersey

was based on the AERMOD model. As part of the review of the NJDEP modeling, EPA also 

concluded that it was necessary to make technical adjustments to the modeling. We therefore 

conducted an independent modeling analysis based on AERMOD that incorporated appropriate 

adjustments to the Portland site specific meteorological data to support the determination of an 

appropriate remedy.

In response to public comments on the proposed rule, the EPA has conducted additional 

dispersion modeling, based on AERMOD. The EPA AERMOD setup was run for the final rule 

with slightly revised stack parameters submitted by GenOn. The final rule AERMOD results 

continue to show violations of the 1-hour SO2 NAAQS in New Jersey from Portland SO2

emissions. EPA calculated the emissions limit needed to eliminate SO2 violations in New Jersey 

and ran AERMOD with the final emissions limits to ensure that no violations were predicted. 

 

1 USEPA promulgated a new 1-hr SO2 NAAQS on June 3, 2010. The NAAQS was set at 75 ppb (about 196 

μg/m3). A violation occurs if the 3 year average of the annual 99th percentile of daily maximum 1 hour average 

values exceeds the level of the NAAQS. 

2

CALPUFF is a non-steady-state puff dispersion model that was originally developed for the California Air 

Resources Board.

3 AERMOD stands for the American Meteorological Society/Environmental Protection Agency Regulatory Model.

4

Docket ID EPA–HQ–OAR–2011–0081-0026
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As a result of comments on the proposed rule, EPA also performed numerous AERMOD runs to 

inform our assessment of the section 126 remedy under reduced load operations at Portland. 

EPA also investigated the potential contribution from meteorological variability in terms of 

Portland’s interference with maintenance of the 1-hour SO2 NAAQS in New Jersey, the viability

of establishing a combined emission limit for Portland units 1 and 2, and assessed model-tomonitor comparisons of ambient SO2 data from the Columbia, New Jersey monitor. The final 

rule AERMOD runs including the additional modeling analyses are documented in the following 

sections and appendices of this final rule Modeling TSD.

Section II of this final rule Modeling TSD summarizes EPA’s modeling for purposes of 

determining an appropriate final remedy for Portland. Section III describes the basis for 

calculating the final remedy for Portland. Section IV describes the modeling conducted to 

demonstrate the adequacy of the final remedy at full and reduced operating loads, as well as the 

viability of a final remedy based on a combined emission limit for units 1 and 2. 

Additional modeling analyses and model evaluations are included in Appendices. These include 

a more detailed analysis of CALPUFF model performance based on inclusion of the PRIME 

building downwash option in CALPUFF for the Martin’s Creek field study evaluation (Appendix 

A), analysis of model-to-monitor comparisons for the Columbia, New Jersey ambient SO2 

monitor which further corroborate the EPA adjustments to the Portland site-specific 

meteorological data and assessment of CALPUFF model performance (Appendix B), and an 

analysis of meteorological variability for purposes of assessing the potential for Portland to 

interfere with maintenance of the 1-hour SO2 NAAQS in New Jersey (Appendix C).
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II. EPA Modeling of Portland Plant for 1-Hour SO2 NAAQS for Determination 

of Remedy

In the proposed rule Modeling TSD we determined that the NJDEP AERMOD modeling was 

sufficient to make a finding that SO2 emissions from the Portland Plant significantly contribute 

to nonattainment or interfere with maintenance of the 1-hr SO2 NAAQs in New Jersey. 

However, we noted some technical concerns with the NJDEP modeling which may affect the 

degree to which emissions need to be reduced to be able to meet the 1-hour SO2 NAAQS in New 

Jersey. Therefore, EPA conducted an independent AERMOD modeling assessment to help 

determine the necessary and appropriate emissions limit for Portland units 1 and 2. 

For the final rule, EPA has made minor updates to the AERMOD modeling used to set the final 

emissions limits for Portland units 1 and 2. This section describes the final rule independent 

analyses conducted by EPA to determine an appropriate remedy to mitigate the ambient impacts 

from the Portland Plant to New Jersey. The data, methods and conclusions from the study are 

summarized below.

A. Emissions and Source Characteristics

This section documents the emissions and source characteristics used by EPA in the dispersion 

modeling conducted to determine the final rule remedy in response to the September 17, 2010 

petition. The discussion below also addresses the issue of whether other nearby emissions 

sources and/or whether background concentrations based on representative monitoring data 

should be included in the modeling analysis. 

As explained in the proposed rule preamble, EPA believes it appropriate to model allowable 

emissions when determining the appropriate remedy to eliminate the source’s significant 

contribution to nonattainment and interference with maintenance. In addition, as a practical 

matter, it would be difficult to determine an appropriate remedy under a section 126 petition 

based on actual emissions given the potential variability of actual emissions. Because the 

question posed is what additional limits must be placed on the source’s emissions to eliminate its 

significant contribution to nonattainment and interference with maintenance, it is appropriate to 

consider what its emissions could be in the absence of such limits. 

Portland Emissions

The dispersion modeling submitted by NJDEP only included emissions from the coal-fired units 

1 and 2 at the Portland Plant. There is also an auxiliary boiler which burns oil and 3 small 

turbines (units 3, 4, and 5) which all burn oil and natural gas, and have very small emissions. 
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Units 1, 2, and 5 utilize continuous emissions monitoring system (CEMS). In 2010, SO2 

emissions combined from units 1 and 2 at the plant were 22,071 tons and emissions from unit 5 

were 0.4 tons. The auxiliary boiler, unit 3, and unit 4 SO2 annual emissions reported in the 2008 

NEI for the auxiliary boiler, unit 3, and unit 4 were 0.01, 0.02, and 0.03 tons, respectively.

Based on the emissions information summarized above, it is necessary to model emissions from 

units 1 and 2. Since unit 5 has CEMS data and an easily obtainable allowable emissions rate, 

EPA also chose to include unit 5 emissions in our modeling analysis. The auxiliary boiler and 

units 3 and 4 have very small emissions and they also do not have an easily discernable 

allowable SO2 emissions rate. Therefore, EPA’s modeling is based only on allowable emissions 

from units 1, 2 and 5 at the Portland Plant. Table 1 shows the allowable emissions from units 1 

and 2 in lbs/hr (which were derived from a tons per 3 hours permit limit). Table 2 shows the 

emissions and stack parameters used in EPA’s AERMOD modeling5.

Table 1. Allowable SO2 Emissions for the Portland Plant

Portland Unit Allowable SO2 Rate Maximum 3-hr permit limit

1 5,820 lb/hr 8.73 tons per 3 hours

2 8,900 lb/hr 13.35 tons per 3 hours

Table 2. SO2 Emissions and stack parameters used in EPA’s AERMOD modeling

Source

Permitted 

Emission 

Rate (g/s)

Stack 

Height 

(m)

Stack 

Diameter 

(m)

Stack 

Temperature 

(K)

Stack Velocity 

(m/s)

Portland Plant 

Coal Unit 1

733.3 121.31 3.15 418.1 32.86

Portland Plant 

Coal Unit 2

1,121.0 121.82 3.84 406.0 34.19

Portland Plant 

Turbine 5

12.0 42.67 6.1 821.5 36.6

Background SO2 Concentrations

Section 8.2 of Appendix W provides guidance regarding the inclusion of background 

concentrations in dispersion modeling demonstrations of compliance with the NAAQS under 

PSD regulations. Appendix W defines “background air quality” as including “pollutant 

 

5 These stack parameters were updated from the proposed rule based on comments submitted by GenOn, 

the current owner of Portland.
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concentrations due to: (1) Natural sources; (2) nearby sources other than the one(s) currently

under consideration; and (3) unidentified sources.” See 40 CFR Part 51, App. W Section 8.2.1a. 

EPA recently issued additional clarification regarding application of Appendix W guidance for 

the 1-hour NO2 NAAQS6, indicating that portions of that guidance are equally applicable to the 

1-hour SO2 NAAQS. Two topics addressed in the March 1, 2011 guidance that are relevant here 

are the determination of background concentrations and combining modeled results with 

monitored background concentrations to determine cumulative impacts. While the guidance 

does not explicitly address dispersion modeling analyses in the context of a section 126 petition, 

we believe that the guidance provides an appropriate basis for the modeling conducted for the 

Portland Plant in support of this action. 

For the proposed rule modeling, EPA used ambient SO2 data from the Chester, NJ monitor to 

derive background concentrations. The monitor is located ~36km southeast of Portland. It is not 

the closet monitor to the source, but it was found to be most representative of background 

concentrations in the area. Additionally, the “Columbia monitor” began operation in September 

2010, located ~2km to the northeast of Portland (in New Jersey). Since the Columbia monitor 

has barely one year of data at this time, it was not used to determine background concentrations 

for the modeling analysis. For the final rule AERMOD modeling, EPA continues to use 

background concentrations derived from the Chester, New Jersey monitor. The proposed rule 

Modeling TSD contains details on the analysis of potential background sources to include in the 

modeling. 

Consistent with the March 1, 2011 guidance, we included monitored concentrations based on the 

99th-percentile by season and hour-of-day from the Chester data for 2007 through 2009 to 

account for background concentrations. These background SO2 concentrations by season and 

hour-of-day varied from 13 ug/m3to 60 ug/m3. Examination of hourly SO2 concentrations for 

both the Chester monitor and the available data from the Columbia monitor indicates very low 

concentrations (less than 3 ppb) during the majority of the hours. However, we consider the 

background concentrations used in our analysis (13 ug/m3to 60 ug/m3) to be appropriate for this 

application given that no other emission sources were explicitly modeled. In addition, as 

discussed in final rule Preamble, we believe that some degree of conservatism in the monitored 

background contribution is appropriate for this analysis in order to account for the potential 

contribution of meteorological variability to Portland’s interference with maintenance of the 1-

hour SO2 NAAQS in New Jersey given the fact that we have only one year of site-specific 

meteorological data available for the modeling analysis. The temporally-varying background 

monitored concentrations incorporated in EPA’s modeling analysis are shown in Figure 1.

 

6

“Additional Clarification Regarding Application of Appendix W Modeling Guidance for the 1-hour 

NO2 National Ambient Air Quality Standard.” Memorandum from Tyler Fox, OAQPS/AQAD, dated 

March 1, 2011.
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Figure 1. Background SO2 monitored concentrations by season and hour-of-day included in the 

AERMOD cumulative modeling analysis for the Portland Plant.

B. Meteorological Data

Aside from emissions data, meteorological data is the other key input to dispersion models. The 

AERMOD modeling was based on one year of site-specific meteorological data collected for 

from a 100m instrumented tower and SODAR located about 2.2km west of the PGS plant, as 

shown in Figure 2, for the period July 1993 through June 1994. 

0

20

40

60

80

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

S

O2 Concentration (p

p

b)

Hour

Chester, NJ Monitor

2007-2009 One-Hour SO2 Concentrations

NAAQS

Ave Winter 99%

Ave Spring 99%

Ave Summer 99%

Ave Summer 99%

Overall 99%

1-hr DV
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Figure 2. Location of Portland Meteorological Tower7

While EPA accepts the use of site-specific meteorological data collected for the Portland Plant 

from 1993-94 as generally appropriate for dispersion modeling of emissions from the Portland 

Plant Units 1 and 2, the 100 meter difference in base elevation between the meteorological tower 

and stack base raises questions regarding the representativeness of the wind data. In the 

proposed rule Modeling TSD, EPA concluded that the representativeness of the Portland Plant 

meteorological data could be improved by some adjustments to the measurement heights from 

the SODAR data and the inclusion of the sigma-w

8

(σw) data collected from the SODAR, which 

 

7

Figure 2 was taken from Appendix A of Exhibit 11 to NJDEP’s May 12, 2010 petition, which was taken 

from the document: SO2 NAAQS Compliance Modeling Protocol for GPU’s Portland Generating Station, 

prepared for GPU Genco, prepared by ENSR Corporation, April 1999.

8

Sigma-w is a measure of vertical turbulence. 
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was not included in NJDEP’s AERMOD modeling. We continue to use these same adjustments 

to the meteorological data for the final rule modeling. 

C. Receptor and Terrain Data

Proper treatment of terrain information is important for this analysis given the potential influence 

of elevated and complex terrain on the modeling results. The NJDEP analysis was based on an 

initial grid of coarsely spaced receptor locations across a large domain covering all potentially 

important impact area associated with emissions from the Portland Plant, followed by a much 

smaller grid of more closely spaced receptors focused on the area of expected worst-case impacts 

from the plant. The initial grid included spacing of 250 meters in areas of expected high impacts 

with receptors spaced at 1,000 meter intervals covering the gaps between the 250m grids. The 

initial coarse receptor grid included a total of 5,189 receptors. The subsequent fine grid used by 

NJDEP in determining the controlling impact from Portland for purposes of this petition 

included a total of 121 receptors in a 10x10 array spaced at 100m intervals covering a portion of 

the Kittatinny Ridge on the New Jersey side of the Delaware Water Gap, where the highest 

impacts from the coarse grid run occurred. The results of EPA’s coarse grid modeling with the 

adjusted meteorological data showed the highest impacts closer to the Portland Plant, about 3km 

northeast of the plant. Based on these coarse grid results, EPA’s fine receptor grids included two 

100m grids, one located near the Kittatinny Ridge similar to NJDEP’s fine grid, and the other 

focused on the area around and toward the northeast of the plant to encompass the location of 

peak impacts from the coarse grid. Figures 2 and 3 depict the location of the initial coarse grid 

and the final fine grid, respectively.

NJDEP applied the AERMAP terrain processor with sixteen 7.5-minute USGS Digital Elevation 

Model (DEM) terrain files at 30m horizontal resolution covering most of the modeling domain 

and four 1-degree DEM files at 90m horizontal resolution covering the remainder of the domain. 

NJDEP also used the NAD27 horizontal datum for all receptor and source coordinates. 

Consistent with the proposed rule EPA modeling, EPA processed the terrain data for the NJDEP 

modeling domain using 1-second NED data (approximately 10m horizontal resolution). The 

source and receptor coordinates were also converted to the NAD83 horizontal datum. As noted 

in the proposed rule Modeling TSD, the terrain elevations and hill height scales generated by 

AERMAP based on the NED data were nearly identical to the corresponding values used in the 

NJDEP analysis.

D. EPA Remedy Modeling Results

The EPA AERMOD modeling results based on the initial coarse receptor grid described above 

indicated a maximum 99th-percentile (4th-highest) daily maximum 1-hour SO2 concentration 

(including monitored background) of 845 μg/m3(about 323 ppb) at a receptor located about 3 
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kilometers north-northeast of the Portland plant (494500m E; 4531500m N). Results from this 

initial analysis are shown in Figure 3. 

Figure 3. EPA modeling results for initial coarse receptor grid.

Compared to the initial coarse grid analysis conducted by NJDEP, EPA’s modeled design value 

is about 32% lower (compared to 1,236 μg/m3) and occurs at a different location within the 

modeling domain. While EPA’s modeling showed peak impacts much lower than NJDEP’s peak 

design value, we note that EPA’s modeled design value of 845 μg/m3is about 90% higher than 

NJDEP’s modeled impact at the controlling receptor from EPA’s modeling. These differences 

are likely due primarily to the adjustments in the processing of meteorological data input to the 

model. The adjustments to the measurement heights could result in significant differences in the 

transport direction for particular hours, as well as somewhat lower wind speeds. Both of these 

factors could shift the modeled impact area away from the higher terrain around the Delaware 

Water Gap toward a different part of the domain. The inclusion of observed σw data from the 

SODAR in the EPA modeling could also account for this shift in the maximum impact area from 

Portland. If observed σw values are higher than the reference values used in AERMOD in the 
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absence of observations, then modeled impacts near the Delaware Water Gap, which are 

associated with direct plume impaction on the complex terrain, could be significantly lower. In 

contrast, larger σw values would tend to increase concentrations in the lower terrain toward the 

northeast by mixing the plume to the ground faster.

Based on the results from the initial coarse grid analysis, EPA developed a finer resolution 

receptor network which included two separate grids with 100m horizontal resolution, shown in 

Figure 4. 

Figure 4. Fine-resolution (100m) receptor grids used in EPA modeling

The smaller of the two fine resolution grids covers the impact area near the Delaware Water Gap 

to the northwest, and is similar to NJDEP’s 100m fine grid, but is extended an additional 500 

meters to the north and east. The larger fine resolution grid is focused on the area surrounding 

the maximum design value from the initial coarse grid, and extends about 5km north, 4 km east, 

1km south and 2km west of the Portland plant. The location of the modeled peak from the 

coarse grid analysis and the recently implemented Columbia Lake monitor are also displayed in 

Figure 4. 
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EPA’s modeling based on the 100m fine receptor grids resulted in modeled design value 

(including background) of 860.8 μg/m3(about 329 ppb). This result is only slightly higher than 

and near the location of the controlling coarse grid result. Figure 5 displays the 99th-percentile 

(4th-highest) 1-hour SO2 concentrations (including background) based on the fine grid analysis.

Figure 5. EPA modeling results for fine-resolution (100m) receptor grid

III. Portland Generating Station Emissions Limits

As detailed above (and shown in Figure 5), the modeled maximum 99th percentile (4th-highest) 

daily maximum 1-hour SO2 concentration (including monitored background) from the Portland 

Plant in New Jersey was 860.8 ug/m3. Table 3 shows the contribution from each of the Portland 

Plant units to the top 5 design value concentrations at receptors in New Jersey.
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Table 3. Contributions from Portland Plant’s units 1, 2, and 5 to modeled design value

Unit 1 Unit 2 Unit 5 Background Total

380.4 ug/m3466.9 ug/m30.4 ug/m313.1 ug/m3860.8 ug/m3

367.4 447.6 1.1 39.3 855.4

384.0 447.1 2.6 30.0 854.7

379.9 460.5 0.3 13.1 853.7

363.1 443.1 1.3 39.3 846.8

Based on this result, EPA calculated the emissions reduction needed to eliminate the Portland 

Plant’s significant contribution to nonattainment in New Jersey. The calculation is relatively

simple in this case because emissions from the Portland Plant alone cause violations of the 1-

hour SO2 NAAQS in New Jersey and background levels of SO2 are very low. If the modeled 

concentration from the Portland Plant plus background is reduced to a level that is below the 1-

hour SO2 NAAQS, then there will be no modeled violations of the NAAQS in New Jersey. 

The following equation was used to calculate the percent emissions reduction needed to reduce 

modeled design values below the NAAQS. 

((Total modeled concentration) – (NAAQS – background))/(total modeled concentration). 

This calculation recognizes that the assumed background concentration cannot be reduced. 

Since the background concentration can vary by receptor and hour, the highest total modeled 

design value may not correspond to the impact that is controlling from the standpoint of 

emissions reductions needed at Portland units 1 and 2. This can be seen when the calculation is 

applied to the 5 high design value receptors from Table 3. The highest design value receptor has 

a relatively low background concentration compared to the 2nd highest receptor. The percent 

reduction from allowable emissions for the 1st high receptor is 78.4%. Due to a higher 

background concentration, the reduction requirement for the 2nd highest receptor is 80.9%. 

Applying the same calculation to the next highest receptors shows that the highest emissions 

reduction remains at 80.9% (which we round to 81%). Therefore, the “controlling receptor” is 

the 2nd highest receptor9 with a total concentration of 855.4 ug/m3. 

The actual calculation based on Table 5 is ((815.0)-(196.2-40.410))/815.0. Therefore, based on 

the EPA modeling results, an 81 percent reduction in allowable SO2 emissions from Portland 

 

9 The 2nd highest receptor (the controlling receptor) is located 100 m north of the 1st high receptor that is 

plotted (with an asterisk) on figure 5.

10 The contribution from unit 5 is only 0.1 percent of the total contribution (1.1 ug/m3

contribution to the 

design value). A reduction in the unit 5 contribution would provide a negligible reduction to the modeled 
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Plant units 1 and 2 is needed to reduce the Portland Plant contribution plus background to below

the NAAQS. Therefore we are finalizing an emissions limit based on an 81 percent reduction in 

allowable emissions at both units 1 and 2. This leads to a final SO2 emissions limit for unit 1 of 

1,105 lbs/hr (5820*0.19) and a final SO2 emissions limit for unit 2 of 1,691 lbs/hr (8900*0.19). 

IV. Demonstration of Adequacy of Remedy at Full and Reduced Operating 

Loads

Full Operating Load Modeling Analysis

As a final check on the emission limit calculations, EPA ran AERMOD again with the above 

final remedy emission limits on the Portland Plant’s units 1 and 2 (including monitored 

background plus current allowable emissions from unit 5). At these final emission levels at full 

load, all receptors in New Jersey were below the 1-hour SO2 NAAQS. The modeled 99th

percentile (4th-highest) daily maximum 1-hour SO2 concentration was 193.7 ug/m3, about 2.5 

ug/m3 below the NAAQS. Figure 6 shows the results of the 81% remedy run, indicating that the 

maximum impact after implementation of the final remedy is shifted to a more easterly trajectory

from Portland and slightly closer to Portland than the peak impact based on current allowable 

emissions.

 

design value. Therefore, it can be assumed that unit 5 emissions do not need to be reduced and should be 

added to the irreducible background value. 
Page 16 of 33

Figure 6. EPA modeling results for the fine resolution (100m) receptor grid with an 81% 

reduction in allowable emissions at Portland units 1 and 2

Reduced Operating Load Modeling Analysis

Although the level of the final remedy, based on an 81% reduction from allowable emissions for 

units 1 and 2, has not changed from the proposed remedy, several additional modeling analyses 

were performed to examine the adequacy of the final remedy at reduced operating loads. In the 

proposed rule, the EPA did not take into account the effect of operating load on stack parameters. 

However, the exit velocity is reduced when the plant is operating below full load. Based on 

information submitted by GenOn as part of its comments (docket ID EPA-HQ-OAR-2011-0081-

0127), the exit velocity could be reduced by as much as 50 percent when operating at or below 

50 percent operating load (defined as percent of maximum heat input for each unit). To account 

for potential reduced plume rise and dispersion due to reduced load or control devices, we 

simulated the proposed remedy emissions rate for units 1 and 2 (1,105 lb/hr unit 1 limit and 

1,691 lb/hr unit 2 limit) at reduced loads of 75 percent, 50 percent, and 25 percent. The exit 

velocity for the reduced load runs was reduced based on information submitted by GenOn. The 
Page 17 of 33

reduced exit velocity led to reduced plume rise and dispersion and higher downwind maximum 

concentration impacts. 

The maximum concentrations at 100 percent operating load is 193.7 ug/m3 as shown in Figure 6 

above. The maximum concentration at 75 percent, 50 percent, and 25 percent load were 227.3 

ug/m3, 264.3 ug/m3, and 300.3 ug/m3respectively (as shown in table 4 below). These impacts all 

exceed the 1-hour SO2 NAAQS. 

Table 4. Design value concentrations with emissions at the final lbs/hr limit at 100% operating 

load, 75% load, 50% load, and 25% load

Unit 1 

Emissions 

Rate 

(lbs/hr)

Unit 2 

Emissions 

Rate 

(lbs/hr)

4th High 

Concentration 

at 100% Load

4th High 

Concentration 

at 75% Load

4th High 

Concentration 

at 50% Load

4th High 

Concentration 

at 25% Load

 

1,106 

 

1,691 193.7 ug/m3 227.3 ug/m3 264.3 ug/m3 300.3 ug/m3

Based on this information the form of the final remedy also includes emission limits in terms of 

pounds per million British thermal units (lb/mmBtu), based on a 30 boiler operating day rolling 

average, in order to ensure that the final remedy is protective of the NAAQS at reduced 

operating loads. The final remedy lb/mmBtu limit is set at 0.67 lb/mmBtu, which is equivalent 

to the final remedy lb/hr limits at full load. The lb/mmBtu limit will become the controlling limit 

at reduced loads, effectively imposing more stringent load-dependent lb/hr limits under those 

conditions. This revision to the final remedy was necessary in order to account for the fact that 

plume rise is reduced under reduced load, resulting in higher ambient impacts under reduced 

load as compared to full load for a specific lb/hr emission rate.

Additional modeling results are summarized in Table 5 showing predicted ambient SO2 impacts 

in New Jersey at full and reduced operating loads, accounting for the corresponding reductions in 

lb/hr emissions based on the final remedy lb/mmBtu limit. The modeled design value 

concentrations at the reduced loads (with a 0.67 lb/mmBtu limit) are all less than the 1-hour SO2 

NAAQS. These additional results confirm that the additional component of the final remedy 

based on a lb/mmBtu limit ensures that emissions from Portland will comply with the NAAQS 

under all load conditions.
Page 18 of 33

Table 5. Design value concentrations with a 0.67 lbs/mmBtu emissions limit at 100% operating load, 75% 

load, 50% load, and 25% load

Scenario

4th High 

Concentration 

at 100% Load

4th High 

Concentration 

at 75% Load

4th High 

Concentration 

at 50% Load

4th High 

Concentration 

at 25% Load

Final emissions limit 

without lb/mmBtu limit11 193.7 227.3 264.3 300.3

Final emissions limit with 

0.67 lb/mmBtu limit12 193.7 178.8 146.0 93.7

Combined Emissions Limit Modeling Analysis

In comments, GenOn had requested a combined limit for units 1 and 2. EPA performed an 

AERMOD analysis which emitted the total emissions limit of 2,796 lbs/hr (1,105 + 1,691 lbs/hr) 

through either unit 1 or unit 2. We found that the total emissions emitted through unit 2 were 

protective of the NAAQS, but due to slightly different stack parameters, the total emissions 

emitted through unit 1 were not protective of the NAAQS. Table 6 shows that the design value 

concentration would be 225.2 ug/m3 if all of the emissions (2,796 lbs/hr) were emitted through 

unit 1 (at 100% operating load13). Therefore, we continue to require individual emissions limits 

at both units 1 and 2. 

Table 6. Design value concentrations with all of the combined unit 1 and 2 emissions emitted 

through a single unit.

Unit 1 Emissions 

Rate (lbs/hr)

Unit 2 Emissions 

Rate (lbs/hr)

4th High 

Concentration at 

100% Load

 2,796 - 225.2

 - 2,796 189.1

 

11 The AERMOD modeling runs without a 0.67 lb/mmBtu limit used the full load lb/hr emissions limits. 

The reduced load scenarios also included proportionally lower exit velocities. 

12 The AERMOD modeling runs with a 0.67 lb/mmBtu limit used the lb/hr emissions limits that were 

scaled by 25%, 50%, and 75% respectively to represent emissions rates at 75%, 50%, and 25% operating 

loads. The reduced load scenarios also included proportionally lower exit velocities. 

13 The modeled concentrations would be even higher (and also not protective of the NAAQS) at reduced 

operating loads).
Page 19 of 33

V. References

EPA, 1992. Protocol for Determining the Best Performing Model. Publication No. EPA–454/ R–

92–025. Office of Air Quality Planning & Standards, Research Triangle Park, NC. (NTIS No. 

PB 93–226082).

EPA, 2000. Meteorological Monitoring Guidance for Regulatory Modeling Applications. EPA 

454/R-99-005. U.S. Environmental Protection Agency, Research Triangle Park, NC (Available 

@ www.epa.gov/scram001/).

EPA, 2003. AERMOD: Latest Features and Evaluation Results. EPA-454/R-03-003. U.S. 

Environmental Protection Agency, Research Triangle Park, NC.

EPA, 2004. User’s Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA-454/B-03-

003. U.S. Environmental Protection Agency, Research Triangle Park, NC (Available @ 

www.epa.gov/scram001/).

EPA, 2008. AERSURFACE User’s Guide. EPA-454/B-08-001. U.S. Environmental Protection 

Agency, Research Triangle Park, NC (Available @ www.epa.gov/scram001/).

EPA, 2009. AERMOD Implementation Guide. U.S. Environmental Protection Agency, 

Research Triangle Park, NC (Available @ www.epa.gov/scram001/)

Perry, S. G., A. J. Cimorelli, R. J. Paine, R. W. Brode, J. C. Weil, A. Venkatram, R. B. Wilson, 

R.F. Lee, and W. D. Peters, 2005. AERMOD: A dispersion model for industrial source 

applications. Part II: Model performance against seventeen field-study databases. J.Appl.Meteor., 

44, 694-708.

Strimaitis, D.G., J.S. Scire and J.C. Chang, 1998. Evaluation of the CALPUFF Dispersion Model 

with Two Power Plant Data Sets. Tenth Joint Conference on the Application of Air Pollution 

Meteorology, Phoenix, Arizona. American Meteorological Society, Boston, MA. January 11–16, 

1998.

Venkatram, A., R. W. Brode, A. J. Cimorelli, J. T. Lee, R. J. Paine, S. G. Perry, W. D. Peters, J. 

C. Weil, and R. B. Wilson, 2001. A complex terrain dispersion model for regulatory

applications. Atmos. Environ., 35, 4211–4221.
Page 20 of 33

Appendix A. 

Analysis of CALPUFF Model Performance Based on Inclusion of PRIME 

Downwash Option for Martin’s Creek

As part of our reassessment of the NJDEP CALPUFF validation results following the 

methodology recommended in the Cox-Tikvart protocol (EPA, 1992) for determining the best 

performing model, we also evaluated CALPUFF model performance based on the use of the 

PRIME downwash option. Figure 1 shows the MCM values for Martin’s Creek based on 

inclusion of the PRIME downwash option within CALPUFF, and based on inclusion of the 

AMS8 monitor, with NJDEP’s meteorological categories, for values of N=26, 15, and 8 in 

calculating RHCs. With PRIME downwash included in CALPUFF, the MCM values show 

larger differences in performance between the two models, as compared to results presented in 

Figure 1 without the use of PRIME in CALPUFF. AERMOD performs “better” than CALPUFF 

for all three values of N, but the confidence intervals are also larger than the results presented in 

Figure 1 and the differences in model performance are still not statistically significant at the 90% 

confidence level. To further illustrate the effect of the PRIME option on CALPUFF results, 

Figure 2 shows an updated 1-hour Q-Q plot that includes CALPUFF results with and without 

PRIME downwash. These figures show that CALPUFF exhibits a greater tendency to 

overestimate concentrations at Martin’s Creek with the PRIME downwash option as compared to 

the ISC-Type downwash option, with some deterioration in the model performance metrics. 

To examine the potential contribution of another issue that the EPA raised regarding the NJDEP 

validation study, Figure 3 compares the MCM values for AERMOD and CALPUFF (with 

PRIME) excluding the AMS8 stack top monitor, which was used only to determine background 

SO2 concentrations in EPA’s evaluation of the AERMOD model using Martin’s Creek. Similar 

to the results shown in Figure 1, AERMOD is shown to perform “better” for all values of N, but 

in this case the difference in performance is statistically significant at the 90% confidence level 

(i.e., the confidence interval does not cross zero) for the cases with N=15 and N=8.

This reassessment of the CALPUFF validation study based on a full implementation of the CoxTikvart protocol, using the modeled and monitored data provided by NJDEP from their 

validation study, supports EPA’s initial assessment that the NJDEP CALPUFF validation study

did not adequately justify the use of CALPUFF in this application under either conditions (2) or 

(3) of Section 3.2.2(b) of Appendix W. 40 CFR Part 51, App. W 3.2.2(b). This assessment also 

highlights the importance of NJDEP’s use of the “ISC-Type” building downwash option in 

CALPUFF, instead of the PRIME downwash option.
Page 21 of 33

Figure 1. Model Comparison Measure for Martin’s Creek Evaluation with PRIME 

Downwash in CALPUFF
Page 22 of 33

Figure 2. Q-Q plot of 1-hour Network-wide SO2 for Martin’s Creek with PRIME option in 

CALPUFF

We have updated Table 11 of the NJDEP CALPUFF Validation Study, summarizing the model 

performance measures for the network peak 1-hour time series comparisons based on the BOOT 

program, to include results based on the use of CALPUFF with the PRIME downwash algorithm. 

Aside from the much higher 1st and 2nd highest modeled concentrations for CALPUFF with 

PRIME, the use the PRIME downwash option in CALPUFF did not dramatically affect the 

conclusions regarding relative performance CALPUFF vs. AERMOD for this study, although 

most performance measures degraded with the inclusion of PRIME downwash in CALPUFF as 

compared to CALPUFF with the ISC-Type downwash option. Note that the revised Table 11 

also corrects a transcription error in the original table for the correlation coefficient for 

CALPUFF. We have also added the results for the normalized mean square error (NMSE), 

another statistical measure that is often used to assess model performance. A “perfect” model 

would have FB and NMSE values of 0.

10

100

1,000

10,000

10 100 1,000 10,000

Predicted Conc (ug/m3)

Observed Conc (ug/m3)

Q-Q Plot of 1-hr Network-wide SO2 for Martin's Creek w/PRIME for CALPUFF

AERMOD

CALPUFF PRIME

CALPUFF ISC-Type
Page 23 of 33

Revised Table 11 from NJDEP Validation Study. Summary of Performance Measures for 

the Network 1-Hour Time Series

Observed

(μg/m3)

CALPUFF 

Model

AERMOD 

Model

CALPUFF 

w/PRIME

Average 40.80 41.71 44.32 42.32

Highest 1823.5 1402.9 1271.8 2409.0

2

nd Highest 1362.4 1240.2 1160.2 2141.3

FAC-2 n/a 0.68 0.68 0.68

Correlation Coef. n/a 0.155 0.128 0.133

NMSE n/a 4.85 4.75 6.16

FB (< 0  over 

pred) n/a -0.022 -0.083 -0.036

FBFN n/a 0.428 0.399 0.430

FBFP n/a 0.450 0.481 0.467

Figure 3. Model Comparison Measure for Martin’s Creek Evaluation with PRIME 

Downwash in CALPUFF and without the AMS8 Stack-top Monitor
Page 24 of 33

We have also updated Table 6 of the NJDEP CALPUFF Validation Study to include the highest1st-high (H1H) and highest-2nd-high (H2H) modeled and observed concentrations for 

CALPUFF and AERMOD and to include CALPUFF model results with the PRIME downwash 

option. Table 6 of the NJDEP CALPUFF Validation Study summarized “network wide 

maximum and second-high monitored values with each model’s actual maximum and secondhigh network wide predictions” based on the Martin’s Creek field study. While the overall 

highest and 2nd highest values reported by NJDEP are consistent with the output from the BOOT 

program summarized in Table 11, it would be more appropriate to report the 1st highest and 

highest-2

nd

-highest values for consistency with the 3-hr and 24-hr regulatory design values for 

SO2 (note that the NJDEP study was conducted before the 1-hour SO2 NAAQS was 

promulgated). Consistent with the updated Q-Q plots shown above, CALPUFF results in the 

revised Table 6 exhibit a more pronounced bias to overestimate concentrations when the PRIME 

downwash option is used.

Revised Table 6 from NJDEP Validation Study. Network Wide Ratios of CALPUFF and 

AERMOD H1H and H2H Modeled to Observed Concentrations, Including CALPUFF 

Results with PRIME Downwash

Averaging 

Time

Observed

(μg/m3)

CALPUFF 

Model

Pred/Obs 

Ratio

AERMOD 

Model

Pred/Obs

Ratio

CALPUFF 

w/PRIME

Pred/Obs

Ratio

H1H 1-hr 1823.5 1402.8 0.77 1271.8 0.70 2409.0 1.32

H2H 1-hr 670.7 1240.2 1.85 1160.2 1.73 1877.3 2.80

H1H 3-hr 710.0 724.6 1.02 563.4 0.79 1620.5 2.28

H2H 3-hr 421.0 554.9 1.32 524.6 1.25 991.6 2.36

H1H 24-hr 185.3 200.9 1.08 165.9 0.90 309.1 1.67

H2H 24-hr 131.2 165.2 1.26 124.2 0.95 143.9 1.10

High Annual 13.1 11.31 0.86 11.15 0.85 10.82 0.83

Geometric 

Mean Ratios 1.12 0.98 1.63

Additional analysis of CALPUFF model performance indicates that CALPUFF exhibited a 

greater tendency to overestimate concentrations for Martin’s Creek under stable conditions when 

the PRIME downwash option was used. Figure 2 presented above shows a revised Q-Q plot of 

network-wide 1-hour SO2 values for AERMOD and CALPUFF, based on PRIME downwash for 

both models, with CALPUFF results based on ISC-Type downwash included for comparison. 

That Q-Q plot clearly shows a greater tendency toward overpredicting the peak values on the 

part of the CALPUFF model when the PRIME downwash option is selected. Figure 4 below 
Page 25 of 33

shows 1-hour Q-Q plots by meteorological category, comparable to the plots presented in Figure 

1 from NJDEP’s comments, but including CALPUFF results based on the use of PRIME 

downwash. These Q-Q plots by stability category indicate that the additional tendency toward 

overprediction for CALPUFF with the PRIME downwash option for Martin’s Creek is 

associated with stable conditions. There’s also an indication that inclusion of PRIME downwash 

in CALPUFF reduced some of the higher concentrations predicted during unstable conditions, 

and CALPUFF results with PRIME drop off more rapidly at the lower end of the distribution 

under neutral conditions.

Figure 4. Q-Q plots of 1-hour Network-wide SO2 for Martin’s Creek Based on 

Meteorological Category with PRIME Option in CALPUFF

References

EPA, 1992. Protocol for Determining the Best Performing Model, U.S. Environmental Protection 

Agency, Research Triangle Park, NC EPA-454/R-92-025
Page 26 of 33

Appendix B. 

Analysis of Model-to-Monitor Comparison for the Columbia, New Jersey 

Ambient SO2 Monitor

NJDEP’s trajectory analysis of episodes of high ambient SO2 concentrations at the Columbia, 

New Jersey ambient SO2 monitor, located about 2 kilometers northeast of the Portland plant 

(NJDEP, 2011), included a comparison of the top 10 AERMOD modeled 1-hour SO2

concentrations based on the 1993-94 site specific meteorological data with the top 10 observed 

1-hour SO2 concentrations reported for the Columbia monitor from September 23, 2010, through 

February 17, 2011. NJDEP’s comparisons of modeled to monitored concentrations at Columbia 

examined three emission scenarios for the Portland Plant, including maximum allowable 

emissions, 50 percent of allowable emissions, and “average” emissions based on the hourly 

emissions from CEMS reported for Portland for the period September through December 2010, 

covering the period when hourly emissions for Portland and Columbia ambient SO2 data were 

both available. Despite the lack of temporal pairing between modeled concentrations based on 

1993-94 site specific meteorological data and monitored concentrations for September 2010 

through February 17, 2011, it is reasonable to expect some degree of comparability between 

modeled and monitored concentrations based on the upper end of the ranked distributions. 

NJDEP found generally good agreement between modeled concentrations based on allowable 

emissions, with modeled concentrations based on “average” emissions being about 50 percent 

less than the monitored concentrations. NJDEP concluded from these comparisons that 

AERMOD modeled concentrations based on allowable emissions “may most accurately reflect 

actual SO2 concentrations in the vicinity of the plant.” 

An additional assessment of the potential contribution to modeled concentrations associated with 

inclusion of SODAR sigma-w data was conducted by the EPA based on NJDEP’s analysis of 

Portland’s impacts on the Columbia, New Jersey ambient SO2 monitor. Given that the Columbia 

monitored data represent ambient SO2 levels at a single location, and the lack of concurrent site 

specific meteorological data for Portland, it would not be meaningful to model the actual hourly 

emissions from the Portland plant in order to compare modeled vs. monitored concentrations 

paired in time and space. In addition, each of the three emission scenarios used in the NJDEP 

analysis are somewhat problematic in terms of their representativeness for capturing the peak of 

the observed concentration distribution. Since the peak observed concentrations are likely to be 

associated with episodes of high emissions, worst-case meteorological conditions, or both, we 

would expect modeling based on average actual emissions to be biased toward underestimating 

observed concentrations since the high emission episodes would not be reflected. On the other 

hand, we would expect modeling based on maximum allowable emissions to be biased toward 

overestimating observed concentrations, unless the plant operates at maximum capacity most of 
Page 27 of 33

the time, which is not the case for Portland. The third emission scenario used by NJDEP based 

on 50 percent of allowable emissions might be a more appropriate scenario for this purpose, 

except for the fact that NJDEP used the same stack parameters as used for the “average” 

emission scenario which represents average loads. A review of the hourly emission data for 

Portland indicates that emissions are typically about 70 percent of “allowable” emissions for a 

given operating load due to the fact that the average sulfur content of the fuel being burned is 

about 70 percent of Portland’s allowable sulfur content. This relationship was generally 

reflected in the “average” emissions scenario used by NJDEP, with 30 percent of allowable 

emissions and 40 percent load for Unit 1 and 38.5 percent of allowable emissions and 51 percent 

load for Unit 2. Based on this review of hourly emissions data for Portland, the EPA included an 

emission scenario based on 100 percent load and 70 percent of allowable emissions for both 

units 1 and 2 as being reasonably representative of peak operating levels during the period of 

monitoring data. However, since Portland frequently operates well below these levels, we would 

expect to see some bias toward overestimation in the modeled concentrations. AERMOD 

predictions based on the EPA adjustments to the Portland meteorological data are consistent with 

that expectation, with an average ratio of predicted/observed concentrations for the top 10 daily

maximum 1-hour values of 1.14. By comparison, the average predicted/observed ratio for 

AERMOD for the same emission scenario using NJDEP’s meteorological data for Portland 

without the EPA’s adjustments was 0.77. 

Table 1 summarizes the results of model-to-monitor comparisons for the Columbia monitor 

conducted by EPA to assess the potential effects on model performance associated with inclusion 

of SODAR sigma-w data and the adjustments to the measurement heights to account for the 

difference in base elevation of the meteorological tower and SODAR relative to the Portland 

stack base elevation, both separately and combined. The monitored and modeled concentrations 

in Table 1 are based on the top 10 daily maximum 1-hour values for consistency with the form of 

the 1-hour SO2 NAAQS, and the monitored values have been updated to reflect observed 

concentrations for a complete year, from September 23, 2010, through September 22, 2011. It is 

unclear whether the modeled and monitored values presented in NJDEP’s analysis of the 

Columbia data are based on the top 10 daily maximum 1-hour values or the overall highest 

hourly values. The results presented in Table 1 show that the average ratio of modeled/predicted 

concentrations for the top 10 daily maximum 1-hour values for AERMOD based on the EPA 

adjustments to the meteorological data is 1.14, indicating very good agreement between modeled 

and monitored concentrations, with a slight bias toward overestimation consistent with the fact 

that the modeled emission scenario represents peak operating conditions during the period. The 

average predicted/observed ratio drops to 0.68 when the SODAR sigma-w data are removed. A 

less significant drop in the average predicted/observed ratio to 0.85 occurred when the SODAR

sigma-w data were included but without EPA’s adjustments to the measurement heights. 

However, the average predicted/observed ratios were even lower at 0.61 when both the SODAR 

sigma-w and measurement height adjustments were excluded. For comparison, the average 
Page 28 of 33

predicted/observed ratio for the 100%-load/70%-allowable emission scenario for AERMOD 

based on the meteorological data used by NJDEP was 0.77. Although these model-to-monitor 

comparisons are based on a single monitoring location and the use of 1993-1994 meteorological 

data for the modeled concentrations vs. 2010-2011 for the monitored concentrations, the results 

tend to corroborate the use of SODAR sigma-w data and other adjustments to the meteorological 

data incorporated in EPA’s AERMOD modeling.

Table 1. Comparisons of daily maximum 1-hour SO2 AERMOD modeled concentrations 

(ug/m3) based on 100% load and 70% allowable emissions vs. Columbia monitored 

concentrations, with and without the EPA adjustments to Portland meteorological data

Daily 

Max 

1-hr 

Rank

Columbia 

Obs Conc

(ug/m3)

AERMOD 

All Adj

(ug/m3)

Pred/Obs 

All Adj

AERMOD 

No sig-w

(ug/m3)

Pred/Obs 

No sig-w

AERMOD 

No H Adj 

(ug/m3)

Pred/Obs 

No H Adj

AERMOD 

No Adj 

(ug/m3)

Pred/Obs 

No Adj

1 479 782 1.633 384 0.802 401 0.839 310 0.648

2 426 531 1.245 337 0.791 379 0.890 263 0.617

3 413 433 1.047 224 0.542 357 0.863 216 0.523

4 356 416 1.169 218 0.614 313 0.881 212 0.595

5 348 356 1.023 212 0.609 296 0.851 210 0.602

6 327 351 1.073 210 0.643 265 0.811 204 0.625

7 306 309 1.010 206 0.673 248 0.810 186 0.609

8 290 301 1.038 205 0.704 248 0.852 182 0.625

9 283 299 1.059 201 0.713 232 0.823 179 0.634

10 277 296 1.068 190 0.684 230 0.830 176 0.635

Ave 350.5 407.4 1.136 238.8 0.678 297.1 0.845 213.8 0.611

Although NJDEP did not include CALPUFF modeled concentrations at the Columbia monitor 

location in the model-to-monitor comparisons presented in their trajectory analysis, the EPA has 

used the NJDEP CALPUFF modeling files for 1992-93 and 2002 that were included with their 

126 petition to generate CALPUFF modeled concentrations at the Columbia monitor location 

using the same Portland source and emission inputs as used in the AERMOD modeling presented 

in Table 1. Results of these additional model-to-monitor comparisons using CALPUFF are 

presented in Table 2 based on the top 10 daily maximum 1-hour concentrations consistent with 

Table 1. The modeled concentrations for AERMOD with the EPA adjustments to the Portland 

site-specific meteorological data are also included for comparison.
Page 29 of 33

Table 2. Comparisons of daily maximum 1-hour SO2 AERMOD and CALPUFF modeled 

concentrations (ug/m3) based on 100% load and 70% allowable emissions vs. Columbia 

monitored concentrations.

Daily 

Max 1-hr 

Rank

Columbia 

Obs Conc

(ug/m3)

AERMOD 

All Adj 

(ug/m3)

AERMOD 

Pred/Obs 

All Adj

CALPUFF 

1992-93 Met 

Data 

(ug/m3)

CALPUFF 

Pred/Obs 

1992-93

CALPUFF 

2002 Met 

Data (ug/m3)

CALPUFF 

Pred/Obs 

2002

1 479 782 1.633 1335 2.788 2406 5.027

2 426 531 1.245 1275 2.990 1841 4.317

3 413 433 1.047 1161 2.808 1759 4.256

4 356 416 1.169 1127 3.168 1331 3.741

5 348 356 1.023 1115 3.203 1305 3.751

6 327 351 1.073 1105 3.380 1100 3.363

7 306 309 1.010 1057 3.454 1070 3.497

8 290 301 1.038 1056 3.637 1048 3.609

9 283 299 1.059 1032 3.654 1015 3.593

10 277 296 1.068 973 3.508 973 3.509

Ave 350.5 407.4 1.136 1123.5 3.259 1384.8 3.866

The CALPUFF modeled concentrations at the location of the Columbia monitor using the 

NJDEP CALMET meteorological inputs for 1992-93 and 2002 exhibit a significant bias toward 

overprediction, with predicted/observed ratios of about 3.3 and 3.9, respectively. This bias 

toward overprediction in CALPUFF is consistent with the significantly more conservative 

modeling results based on CALPUFF as compared to AERMOD submitted with the NJDEP 126 

petition for Portland. Although we acknowledge the limitations of these model-to-monitor 

comparisons given the different time periods associated with the modeled and monitored 

concentrations, and the limitations associated with comparisons at a single locations, we believe 

that these additional model-to-monitor comparisons using the Columbia, New Jersey monitoring 

data provide additional support for the EPA position that AERMOD is a more appropriate model 

for this application than CALPUFF.

References:

NJDEP, 2011. Analysis of the Sulfur Dioxide Measurements from the Columbia Lake NJ 

Monitor. Bureau of Technical Services, Division of Air Quality, New Jersey Dept. of 

Environmental Protection, March 4, 2011. Docket ID No. EPA-HQ-OAR-2011-0081-0019
Page 30 of 33

Appendix C. 

Analysis of Meteorological Variability

Based on AERMOD dispersion modeling of SO2 emissions from Portland, the EPA determined 

that the modeled concentrations from Portland, when combined with the relatively low 

background concentrations, cause violations of the 1-hour SO2 NAAQS in New Jersey. In the 

final rule, the EPA has defined Portland’s significant contribution to nonattainment and 

interference with maintenance of the 1-hour SO2 NAAQS in New Jersey as those emissions that 

must be eliminated to bring the downwind receptors in New Jersey affected by Portland into 

modeled attainment in the analysis year. The EPA also analyzed the modeling results to 

determine the appropriate emissions reductions that were needed to eliminate “interfere with 

maintenance.” 

Meteorological variability is an important aspect of interference with maintenance due to the fact 

that the modeled concentrations which define a source’s significant contribution to 

nonattainment are based on historical meteorological data and cannot explicitly account for that 

source’s impacts under future meteorological conditions. The EPA acknowledged in the 

proposed rule, and public comments also noted, that this issue may have some additional 

importance in this case due to the fact that only one year of site-specific meteorological data was 

available to support the dispersion modeling analysis. 

The question regarding meteorological variability is whether impacts from Portland emissions 

could be higher in the future due to differences in meteorological conditions of importance to 

transport and dispersion of emissions from Portland, and if so, how much higher would they

likely be under the worst-case assumption that the meteorological data (in this case one year of 

available site-specific data) represent the least conservative (or most favorable) meteorological 

conditions. 

Although we are not able to explicitly account for the impact of year-to-year variability of 

meteorology on downwind modeled concentrations based on the available site-specific data, the 

form of the 1-hour SO2 NAAQS based on the 99th percentile of the annual distribution of daily

maximum 1-hour values, averaged across 3 years for monitoring data, is recognized as a more 

stable metric of ambient air quality that is less sensitive to meteorological variability than a 

deterministic standard that would be based on allowing one exceedance per year. For a 

deterministic standard, the inclusion of additional years of meteorological data can only increase 

the modeled design value or leave it unchanged, since the design value is the highest of the 

second-highest values across each of the individual years modeled. In contrast, the inclusion of 

additional years of meteorological data for a probabilistic standard such as the 1-hour SO2 

NAAQS may increase or decrease the modeled design value since it is averaged across the 

number of years modeled at each modeled receptor. 
Page 31 of 33

To further illustrate this point, the EPA performed an analysis of modeled impacts from Portland 

based on 5 years of meteorological data from the Allentown National Weather Service (NWS) 

station for the period 2006 through 2010. The Allentown NWS station is located about 40 km 

southwest of Portland, essentially upwind of the maximum modeled impacts from Portland in 

New Jersey. Although we believe that the use of five years of Allentown meteorological data 

will provide meaningful information in relation to the potential contribution of meteorological 

variability for Portland, we believe that the use of one year of Portland site-specific data is 

preferred as the basis for estimating ambient impacts from Portland’s SO2 emissions. The use of 

one year of site-specific meteorological data fulfills the requirements of Appendix W related 

modeling demonstrations of compliance with the NAAQS. Furthermore, Appendix W expresses 

a clear preference for the use of site-specific meteorological over NWS data, even in cases where 

only one year of data is available14. Therefore, the modeling analyses presented here are strictly

to examine the issue of how meteorological variability should be treated with respect to 

interference with maintenance. This analysis does not necessarily imply that the Allentown 

meteorological data are adequately representative for purposes of regulatory modeling under 

Appendix W to estimate Portland’s impacts in New Jersey.

The AERMOD modeling analysis utilized five years of Allentown NWS data in the TD-3505 

(aka ‘ISHD’ or DS3505) format, containing the standard surface weather observations reported 

for each hour, including temperature, wind speed and direction, cloud cover, etc. We also 

supplemented the standard Allentown surface data with hourly average wind speed and direction 

derived from the 1-minute ASOS wind data (TD-6405 format), which were processed through 

the EPA AERMINUTE program for input to the AERMET meteorological processor. Upper air 

(radiosonde) data from the Albany, New York station in the FSL format were also used as input 

to AERMET. 

Surface characteristics necessary for input to AERMET, including albedo, Bowen ratio, and 

surface roughness, were derived from a Beta version of AERSURFACE using 1992 National 

Land Cover Data (NLCD). The surface roughness estimates were based on the location of the 

Allentown NWS station meteorological tower while the estimates of albedo and Bowen ratio 

were based on the location of Portland, consistent with guidance in Section 3.1.2 of the EPA’s 

AERMOD Implementation Guide (EPA, 2009). Surface roughness estimates for Allentown were 

also estimated based on application of a gust factor methodology (Weiringa, 1980; EPA, 2011), 

utilizing 10-minute average wind speeds and 10-minute peak gusts from the 1-minute ASOS 

wind data. The AERSURFACE and gust factor estimates of surface roughness were both based 

on eight 45-degree sectors centered on North, Northeast, East, etc. The AERSURFACE and gust 

factor estimates of surface roughness showed reasonably good agreement. However, based on 

comparisons of AERSURFACE and gust factor estimates of surface roughness and a review of 

available satellite imagery of the Allentown NWS station location, we adjusted the surface 

 

14 See 40 CFR Part 51, Appendix W Section 8.3.1.2.
Page 32 of 33

roughness estimates based on AERSURFACE for sectors 3 (southeast) and 7 (northwest) to be 

more consistent with the gust factor estimates and actual land cover characteristics for those 

sectors. 

The AERMOD model was applied using the AERMET-processed meteorological data for 

Allentown using the current allowable emissions from Portland for the 6km by 6km finelyspaced (100m) receptor grid focused on the area of peak impacts from Portland northeast of the 

plant. Since the purpose of this analysis was to assess the potential contribution due to 

meteorological variability to ambient impacts associated with Portland SO2 emissions, the 

AERMOD modeling did not include the monitored background concentrations incorporated in 

the modeling conducted in support of the section 126 finding and remedy for Portland.

Modeled concentrations were generated for the five-year period of 2006 through 2010, and for 

each individual year within that range. The five-year modeling results are comparable to a 

modeling analysis conducted in support of a PSD permit. The most straightforward and simplest 

approach for estimating the potential contribution of meteorological variability, that is consistent 

with the form of the 1-hour SO2 NAAQS, is to compare the lowest individual year modeled 

design value15 with the five-year averaged modeled design value. This approach should provide a 

reasonable indication of the “worst-case” potential contribution from meteorological variability

for this specific application.

The results of this analysis are summarized in Table 1, which shows that the range of variability

between the individual year with the lowest modeled design value and the 5-year average 

modeled design value based on the form of the 1-hour SO2 NAAQS is about 6 percent. For 

comparison, the range of variability across the 5 years for a deterministic 1-hour standard, using 

the same 5 years of meteorology data, was about 35 percent for the first highest 1-hour values 

and about 17 percent for the highest second-highest 1-hour values. Note that the modeled 

concentrations presented in Table 1 have been normalized based on a five-year unitless value of 

100. 

 

15 The 99th percentile (4th highest) of the annual distribution of daily maximum 1-hour values, averaged 

across the number of years modeled.
Page 33 of 33

Table 1. AERMOD Modeling Related to Meteorological Variability for Portland based on 2006-

2010 Allentown, PA Meteorological Data

1-hour SO2 Probabilistic NAAQS 1-hour Deterministic NAAQS (H2H design value)

Data Period 99th% daily max 

1hr

Data Period

High-1

st

-High 1hr High-2

nd

-High 1hr

2006-2010 5-yr 100.0 2006-2010 5-yr 100.0 100.0

2006 – Year 1 101.6 2006 – Year 1 76.9 86.7

2007 – Year 2 94.1 2007 – Year 2 73.6 86.0

2008 – Year 3 95.6 2008 – Year 3 100.0 88.9

2009 – Year 4 103.3 2009 – Year 4 72.6 85.8

2010 – Year 5 106.6 2010 – Year 5 87.7 100.0

References

Wieringa, J., 1980. Representativeness of wind observations at airports. Bulletin American 

Meteorological Society. Bulletin American Meteorological Society, 61, 962-971. 

EPA, 2009. AERMOD Implementation Guide. U.S. Environmental Protection Agency, Research 

Triangle Park, NC (Available @ www.epa.gov/scram001/)

EPA, 2011. AERSURFACE Update. Presentation at 2011 Regional/State/Local Modelers 

Workshop, Atlanta , GA, June 2011. Available at: 

http://www.cleanairinfo.com/regionalstatelocalmodelingworkshop/archive/2011/Presentations/4-

Wednesday_AM/4-2_Brode_RSL2011_AERSURFACE_Update.pdf